#Get the data Here we look at 538 Halloween candy. We take the CSV
url <-"https://raw.githubusercontent.com/fivethirtyeight/data/master/candy-power-ranking/candy-data.csv"
candy <- read.csv(url,row.names=1)
head(candy, n=5)
## chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand 1 0 1 0 0 1
## 3 Musketeers 1 0 0 0 1 0
## One dime 0 0 0 0 0 0
## One quarter 0 0 0 0 0 0
## Air Heads 0 1 0 0 0 0
## hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand 0 1 0 0.732 0.860 66.97173
## 3 Musketeers 0 1 0 0.604 0.511 67.60294
## One dime 0 0 0 0.011 0.116 32.26109
## One quarter 0 0 0 0.011 0.511 46.11650
## Air Heads 0 0 0 0.906 0.511 52.34146
Q1.
nrow(candy)
## [1] 85
Q2.
sum(candy$fruity)
## [1] 38
#2. What is your favorite candy?
candy["Snickers",]$winpercent
## [1] 76.67378
Q4.
candy["Kit Kat",]$winpercent
## [1] 76.7686
Q5.
candy["Tootsie Roll Snack Bars",]$winpercent
## [1] 49.6535
library("skimr")
skim(candy)
| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset? Win percent
Q7. What do you think a zero and one represent for the candy$chocolate column?
Q8. Plot a histogram of winpercent values
hist(candy$winpercent)
Q9. Is the distribution of winpercent values symmetrical? No
Q10. Is the center of the distribution above or below 50%? Below
Q11. On average is chocolate candy higher or lower ranked than fruit candy? Chocolate candy is higher ranked than fruity candy
First we need to final all the chocolate candy rows in my ‘candy’ data
inds<- as.logical(candy$chocolate)
candy[inds,]$winpercent
## [1] 66.97173 67.60294 50.34755 56.91455 38.97504 55.37545 62.28448 56.49050
## [9] 59.23612 57.21925 76.76860 71.46505 66.57458 55.06407 73.09956 60.80070
## [17] 64.35334 47.82975 54.52645 70.73564 66.47068 69.48379 81.86626 84.18029
## [25] 73.43499 72.88790 65.71629 34.72200 37.88719 76.67378 59.52925 48.98265
## [33] 43.06890 45.73675 49.65350 81.64291 49.52411
inds<- as.logical(candy$chocolate)
chocolate<- candy[inds,]$winpercent
inds.fruit<- as.logical(candy$fruity)
fruity<- candy[inds.fruit,]$winpercent
mean(chocolate)
## [1] 60.92153
mean(fruity)
## [1] 44.11974
Q12. Is this difference statistically significant? Yes
t.test(chocolate, fruity)
##
## Welch Two Sample t-test
##
## data: chocolate and fruity
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.44563 22.15795
## sample estimates:
## mean of x mean of y
## 60.92153 44.11974
#3. Overall Candy Rankings
Q13. What are the five least liked candy types in this set? Nik L Nip, Boston Baked Beans, Chiclets, Super Bubble, Jawbusters
head(candy[order(candy$winpercent),], n=5)
## chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip 0 1 0 0 0
## Boston Baked Beans 0 0 0 1 0
## Chiclets 0 1 0 0 0
## Super Bubble 0 1 0 0 0
## Jawbusters 0 1 0 0 0
## crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip 0 0 0 1 0.197 0.976
## Boston Baked Beans 0 0 0 1 0.313 0.511
## Chiclets 0 0 0 1 0.046 0.325
## Super Bubble 0 0 0 0 0.162 0.116
## Jawbusters 0 1 0 1 0.093 0.511
## winpercent
## Nik L Nip 22.44534
## Boston Baked Beans 23.41782
## Chiclets 24.52499
## Super Bubble 27.30386
## Jawbusters 28.12744
Q14. What are the top 5 all time favorite candy types out of this set?
head(candy[order(candy$winpercent),], n=)
## chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip 0 1 0 0 0
## Boston Baked Beans 0 0 0 1 0
## Chiclets 0 1 0 0 0
## Super Bubble 0 1 0 0 0
## Jawbusters 0 1 0 0 0
## Root Beer Barrels 0 0 0 0 0
## crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip 0 0 0 1 0.197 0.976
## Boston Baked Beans 0 0 0 1 0.313 0.511
## Chiclets 0 0 0 1 0.046 0.325
## Super Bubble 0 0 0 0 0.162 0.116
## Jawbusters 0 1 0 1 0.093 0.511
## Root Beer Barrels 0 1 0 1 0.732 0.069
## winpercent
## Nik L Nip 22.44534
## Boston Baked Beans 23.41782
## Chiclets 24.52499
## Super Bubble 27.30386
## Jawbusters 28.12744
## Root Beer Barrels 29.70369
Q15. Make a first barplot of candy ranking based on winpercent values.
library(ggplot2)
ggplot(candy)+
aes(winpercent, rownames(candy))+
geom_col()
Make it better >Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?
ggplot(candy)+
aes(winpercent, reorder(rownames(candy), winpercent))+
geom_col()
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "red"
my_cols[as.logical(candy$bar)] = "orange"
my_cols[as.logical(candy$fruity)] = "brown"
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols)
>Q17. What is the worst ranked chocolate candy? Sixlets
Q18. What is the best ranked fruity candy? Starburst
#4. Taking a look at pricecenter
library(ggrepel)
# How about a plot of price vs win
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3, max.overlaps = 5)
## Warning: ggrepel: 53 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )
## pricepercent winpercent
## Nik L Nip 0.976 22.44534
## Nestle Smarties 0.976 37.88719
## Ring pop 0.965 35.29076
## HersheyÕs Krackel 0.918 62.28448
## HersheyÕs Milk Chocolate 0.918 56.49050
Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck? Reese’s Minatures
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular? a. Nik L Nip, Nestle Smarties, Ring pop, Hershey’s Krackel. Hershey’s Milk Chocolate b. Nik L Nip
#5. Exploring the correlation structure
library(corrplot)
## corrplot 0.90 loaded
cij <- cor(candy)
corrplot(cij)
>Q22. What two variables are anti-correlated? chocolate and fruity
Q23. What two varibles are most positvely correlated? chocolate and winpercent
#6. Principal Component Analysis
pca<- prcomp(candy, scale=TRUE)
summary(pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000
plot(pca$x[,1:2], col=my_cols)
plot(pca$x[,1:2], col=my_cols, pch=16)
# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])
p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols)
p
library(ggrepel)
p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")
## Warning: ggrepel: 41 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly(p)
par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")
Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you? Fruity, hard, pluribus